“Spread the Word”: Creepypasta, Hauntology, and an Ethics of the Curse
Bibliographic record
Abstract
According to Internet legend, a cursed JPEG file circulates online, featuring an image of a dog with a much too human grin. If you happen to see this image, the dog will haunt your dreams, asking you to “spread the word” by showing its picture to someone else, thereby passing on the curse. The story of Smile.dog, which is the demon dog's name, is a so-called creepypasta – that is, a digital urban legend. Its curse is therefore a playful one, meant to be circulated as a hoax, but it is also a productive, yet challenging, place to ruminate upon ethics in an era of digital media. Through the lens of Jacques Derrida's concept of hauntology – a haunted ontology – this article explores what digital monsters and curses might teach us about ethics as a question of responding to that which haunts and hoaxes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.099 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".